Covid Report for Family

Background

Notes and format last updated May 7, 2020

Starting on the May 7th update, the NY Times began including probable covid cases/deaths along with confirmed. This mostly affects death counts – for certain geographies that include probable COVID deaths in addition to confirmed, these are now added to the totals. For the time being, they were all added to the May 6th totals, causing a big spike at the U.S. level. Over time, NY Times will revise their historical counts and distribute these added deaths when they actually occurred, so the spike should fade.

• This page shows COVID data broken down into regions that affect our family. It pulls from publicly available raw data from the NY Times, which is reported daily. It’s on a one-day lag, so when you see this report, the most recent day included will be that reported the day prior.
• The table of contents above summarizes the major sections and can be used to jump to those sections.
• Each major section is then divided into tabs to allow you to more easily shuffle between geographies and metrics.
  • If you open on your phone, I don’t believe the tabs will work, so you’ll just see the page as one long, continuous document.
• More detailed descriptions and context can be found in each section.

Growth rates

Heat maps

• The two heat maps below compare how quickly total cases or deaths have grown at various times in our respective geopgraphies.
• The first plot compares growth rate for total cases; the second, growth rate for total deaths.
• The metric used is doubling time, by which I mean how quickly total cases or deaths are doubling.
• The plots track that doubling time at each date for our geographies. Darker colors reflect shorter doubling times, and thus periods of faster growth.
  • You can use the plots to track each geography over time and to compare the geographies to one another.
  • You can also compare the cases and death charts, to see how faster periods of death growth follow faster periods of case growth.

Case growth rates

• This section charts the growth rate of both total and new cases for each of our respective geographies. Each geography has its own chart, and then that chart will have a trendline for total cases and new cases.
  • There are only plots for the U.S. and states because the numbers for the counties are too small to generate worthwhile trendlines in this section.
• Note that we’re charting growth rate and not a count of cases, so don’t think of these as the standard “curve” that we hear about in the news and that we want to flatten. Instead, these growth rate charts help track more precisely what we can only estimate when we see those other curves. For these growth rate charts, if the line is above zero, the metric we are tracking (total or new cases) is continuing to grow. If the growth rate line is going up, it’s growing more quickly each day; if it’s going down but still above zero, it’s growing less quickly (but still growing). Only when the growth rate lines go below zero has the metric stopped growing.
• Each of these two lines uses rolling windows to calculate a growth rate for that particular metric. I do the calculation differently for each to smooth out some of the large day-to-day discrepancies in new case reporting at the state level.
  • For total cases, the trendlines are a rolling 3-day average of daily growth rates in total cases. We want to see these decline (and almost all are), but they can’t go below zero. This is because we’re tracking growth rate and a growth rate line below zero would mean total cases have gone down, which can’t happen. They can only grow less quickly, which means we want to see the total case line get as close to zero as possible.
  • For new cases, the trendlines show a rolling 3-day average of daily growth rate in the rolling 7-day average of new cases. Including two rolling periods in this average helps smooth out crazy spikes at the state level that result from large day-to-day changes. Unlike the lines for total cases, we want to watch for the lines for new cases to get consistently below zero and stay there. That means that we are consistently seeing fewer new cases on a daily basis.

U.S.

Our states

Death growth rates

• This section charts the growth rate of both total and new deaths for each of our respective geographies. Each geography has its own chart, and then that chart will have a trendline for total deaths and new deaths.
  • There are only plots for the U.S. and states because the numbers for the counties are too small to generate worthwhile trendlines in this section.
• Note that we’re charting growth rate and not a count of deaths, so don’t think of these as the standard “curve” that we hear about in the news and that we want to flatten. Instead, these growth rate charts help track more precisely what we can only estimate when we see those other curves. For these growth rate charts, if the line is above zero, the metric we are tracking (total or new deaths) is continuing to grow. If the growth rate line is going up, it’s growing more quickly each day; if it’s going down but still above zero, it’s growing less quickly (but still growing). Only when the growth rate lines go below zero has the metric stopped growing.
• Each of these two lines uses rolling windows to calculate a growth rate for that particular metric. I do the calculation differently for each to smooth out some of the large day-to-day discrepancies in new death reporting at the state level.
  • For total deaths, the trendlines are a rolling 3-day average of daily growth rates in total deaths. We want to see these decline (and almost all are), but they can’t go below zero. This is because we’re tracking growth rate and a growth rate line below zero would mean total deaths have gone down, which can’t happen. They can only grow less quickly, which means we want to see the total death line get as close to zero as possible.
  • For new deaths, the trendlines show a rolling 3-day average of daily growth rate in the rolling 7-day average of new deaths. Including two rolling periods in this average helps smooth out crazy spikes at the state level that result from large day-to-day changes. Unlike the lines for total deaths, we want to watch for the lines for new deaths to get consistently below zero and stay there. That means that we are consistently seeing fewer new deaths on a daily basis.

U.S.

Our states

By population rankings

This section tracks metrics for states and counties normalized for population (number of cases or deaths per million residents), and then compares these figures both for our geographies and the country overall.

States

• This section shows tables ranking all 50 states for per populations rates of total cases, new cases, total deaths, and new deaths.
• For each metric, in addition to the tables, the trends for the top states are plotted over time.
  • We only plot the top ten states for each metric so that the plots aren’t too crowded. But you can view the full 50-state rankings in the tables.

Total confirmed cases

Table of total confirmed cases per million residents (all 50 states)

Ranking	State	Cases Per Million
1	North Dakota	38,918
2	Louisiana	38,064
3	Mississippi	36,335
4	South Dakota	35,055
5	Florida	34,686
6	Alabama	34,500
7	Iowa	33,036
8	Tennessee	32,120
9	Arkansas	31,984
10	Arizona	31,426
11	South Carolina	31,290
12	Georgia	30,270
13	Wisconsin	29,451
14	Texas	29,435
15	Idaho	28,801
16	Nebraska	28,653
17	Nevada	28,596
18	Utah	28,224
19	Illinois	26,532
20	Oklahoma	26,241
21	Rhode Island	25,900
22	Missouri	25,440
23	New York	24,886
24	New Jersey	24,631
25	Kansas	24,307
26	Delaware	23,167
27	District of Columbia	22,906
28	North Carolina	22,819
29	Maryland	22,171
30	California	22,028
31	Indiana	21,332
32	Minnesota	20,774
33	Massachusetts	20,541
34	Montana	19,688
35	Kentucky	19,341
36	Virginia	19,089
37	Puerto Rico	17,561
38	Connecticut	17,397
39	New Mexico	16,671
40	Michigan	15,713
41	Alaska	15,403
42	Ohio	15,043
43	Wyoming	14,470
44	Colorado	14,316
45	Pennsylvania	14,255
46	Washington	13,242
47	West Virginia	10,647
48	Hawaii	9,832
49	Oregon	9,136
50	New Hampshire	6,932
51	Maine	4,341
52	Vermont	3,049

New confirmed cases

Table of new cases per million residents: rolling 3-day average (all 50 states)

Ranking	State	New Cases Per Million
1	North Dakota	838
2	South Dakota	786
3	Wisconsin	619
4	Montana	573
5	Idaho	523
6	Nebraska	446
7	Iowa	399
8	Utah	375
9	Missouri	362
10	Rhode Island	359
11	Arkansas	335
12	Wyoming	330
13	Mississippi	326
14	Oklahoma	307
15	Illinois	275
16	New Mexico	253
17	Tennessee	249
18	Kentucky	248
19	Indiana	231
20	Minnesota	216
21	Alaska	210
22	Alabama	209
23	North Carolina	199
24	South Carolina	197
25	Texas	196
26	Puerto Rico	193
27	Kansas	189
28	Michigan	188
29	Nevada	178
30	Colorado	170
31	Louisiana	165
32	Ohio	161
33	West Virginia	148
34	Georgia	141
35	Florida	139
36	Virginia	131
37	Arizona	122
38	Pennsylvania	121
39	Washington	112
40	New Jersey	108
41	Delaware	92
42	Maryland	92
43	Massachusetts	92
44	California	87
45	Oregon	84
46	New York	70
47	District of Columbia	68
48	Connecticut	60
49	Hawaii	58
50	New Hampshire	53
51	Maine	20
52	Vermont	14

Total deaths

Table of total deaths per million residents (all 50 states)

Ranking	State	Deaths Per Million
1	New Jersey	1,823
2	New York	1,693
3	Massachusetts	1,403
4	Connecticut	1,273
5	Louisiana	1,227
6	Rhode Island	1,084
7	Mississippi	1,059
8	District of Columbia	904
9	Arizona	795
10	Illinois	741
11	Florida	732
12	Michigan	731
13	South Carolina	700
14	Georgia	689
15	Delaware	678
16	Maryland	666
17	Pennsylvania	663
18	Texas	598
19	Indiana	573
20	Alabama	562
21	Nevada	551
22	Arkansas	545
23	North Dakota	492
24	Iowa	480
25	New Mexico	439
26	Ohio	430
27	California	426
28	Tennessee	415
29	Missouri	412
30	Minnesota	399
31	Virginia	396
32	Colorado	379
33	North Carolina	371
34	South Dakota	343
35	New Hampshire	340
36	Washington	306
37	Kentucky	300
38	Idaho	291
39	Oklahoma	288
40	Kansas	287
41	Nebraska	282
42	Wisconsin	269
43	Puerto Rico	232
44	West Virginia	219
45	Montana	215
46	Utah	165
47	Oregon	145
48	Hawaii	129
49	Maine	107
50	Wyoming	98
51	Vermont	92
52	Alaska	83

New deaths

Table of new deaths per million residents: rolling 3-day average (all 50 states)

Ranking	State	New Deaths Per Million
1	North Dakota	10
2	Kansas	7
3	Alabama	6
4	Arkansas	6
5	South Dakota	6
6	Florida	5
7	Mississippi	5
8	Montana	5
9	Rhode Island	5
10	Tennessee	4
11	Wisconsin	4
12	Hawaii	3
13	Illinois	3
14	Indiana	3
15	Iowa	3
16	Kentucky	3
17	Minnesota	3
18	Nevada	3
19	North Carolina	3
20	Oklahoma	3
21	South Carolina	3
22	Texas	3
23	Alaska	2
24	California	2
25	Colorado	2
26	Louisiana	2
27	Massachusetts	2
28	Michigan	2
29	Missouri	2
30	Nebraska	2
31	Arizona	1
32	Delaware	1
33	Georgia	1
34	Idaho	1
35	Maryland	1
36	New Hampshire	1
37	New Mexico	1
38	Oregon	1
39	Pennsylvania	1
40	Virginia	1
41	Washington	1
42	West Virginia	1
43	Wyoming	1
44	Connecticut	0
45	District of Columbia	0
46	Maine	0
47	New Jersey	0
48	New York	0
49	Ohio	0
50	Puerto Rico	0
51	Utah	0
52	Vermont	0

Counties

• This section focuses on the county level. It shows tables with our counties ranked by percentile of U.S. counties for per population rates of total cases and total deaths.
  • Each table also shows the top five counties in the country in addition to our counties, for added perspecive.
• In addition to the tables, our counties’ percentile for both total cases and total deaths are plotted over time.

Confirmed cases

Table showing total cases per million and percentile for all US counties. Includes our counties and the top 5 in the US for perspective.

County	State	Cases Per Million	Raw Ranking	Percentile
Lincoln	Arkansas	166,001	1	99
Chattahoochee	Georgia	157,514	2	99
Trousdale	Tennessee	152,162	3	99
Lafayette	Florida	148,896	4	99
Lake	Tennessee	135,975	5	99
Davidson	Tennessee	44,675	241	92
Richland	South Carolina	39,364	362	88
York	South Carolina	21,614	1378	56
Orange	California	18,321	1654	47
Pierce	Washington	11,132	2382	24

Our county percentiles over time

Deaths

Table showing total deaths per million and percentile for all US counties. Includes our counties and the top 5 in the US for perspective.

County	State	Deaths Per Million	Raw Ranking	Percentile
Hancock	Georgia	5,203	1	99
Kenedy	Texas	4,950	2	99
Emporia city	Virginia	4,863	3	99
Galax city	Virginia	4,727	4	99
Randolph	Georgia	4,279	5	99
Richland	South Carolina	621	775	75
Davidson	Tennessee	467	1103	64
Orange	California	438	1169	62
York	South Carolina	320	1483	52
Pierce	Washington	266	1667	46

Our county percentiles over time

Raw counts

• This section shows the raw counts over time for the U.S., our states, and our counties for four categories:
  • Total confirmed cases: This is the cumulative total of cases reported.
  • New confirmed cases: This is the number of new cases reported on each respective date.
    • For the U.S. and states, a trendline shows the rolling 7-day average of new cases, to help better visualize the peak.
  • Total deaths: This is the cumulative total of deaths reported.
  • New deaths: This is the number of new deaths reported on each respective date.
    • For the U.S. and states, a trendline shows the rolling 7-day average of new deaths, to help better visualize the peak.

Total confirmed cases

U.S.

Our states

Our counties

New confirmed cases

U.S.

Our states

Our counties

Total deaths

U.S.

Our states

Our counties

New deaths

U.S.

Our states

Our counties

Stay-at-home comparisons

• This section is a work in progress. It’s exploring what effect stay-at-home orders have on spread. It’s inherentely messy because some states have localized orders but not orders for their full state and because states implemented the orders at different times.
  • The first plot shows a rolling 3-day average of new cases per million residents, averaged across 2 groups of states – those with a state-wide stay-at-home order in effect and those without. The size of the dots reflect the number of states in each group for each given day (so the dots are getting bigger over time for the group with orders, as more states implement those orders).
    • So far, this view doesn’t show if these orders are “working” or not. Instead, the biggest takeaway from the current plot is that the states that implemented orders are reacting to having worse spread, which is why the case rate is so much higher at the start for them. As a state decides it has a problem and implements an order, it joins the grouping of states with orders and drives the rate up for that group.
    • Over time (if the policies work), what we’d expect is that the line for the group with orders flattens or declines in relation to the no-order group. It also may become moot if all states issue orders.
    • As states start rescending their orders, I’ll put them back into the no-order group, so the no-order group should beging to expand again.
  • The second plot tracks progress of states by the number of days since orders have been in effect. It includes only those states with orders and shows their average new case growth rate (a rolling 3-day average of daily growth rates) based on how long it’s been since the order went into effect. Again, the size of the dots show how many states are included in each point – i.e., as you go further right, the dots are smaller because less states are that far past their order.
    • I’m working on how to add a baseline to this chart to compare to states without orders, but that’s challenging because there’s no way to fully align timing.